What to Expect in UC Berkeley MFE Technical Interview (2025 Cycle)?

[eleayace]

Hi everyone - I’m scheduled for the UC Berkeley MFE technical interview soon and would appreciate hearing from those who have recently gone through it, esp. within the 2024, 2025 admissions cycles.

Could you share:

• The format (e.g., number of questions, duration, Python/C++ coding vs. math/finance balance)
• Whether questions emphasize probability/statistics, derivatives/PDE, data/backtesting, or general coding logic
• How much interviewers probe follow-ups (clarifications, efficiency analysis, model explanation) versus expecting short answers

Not asking for question spoilers ... I’ll compile anonymized notes afterward so ppl in future cycles might benefit.

With many thanks and appreciations.

 

[Andy Nguyen]

Alumni from the program will interview you. They will go off your resume so anything you put there will be fair game.
No interview is the same. It depends on who interviews you and what background or topics s/he is most familiar with.

 

[eleayace]

Alumni from the program will interview you. They will go off your resume so anything you put there will be fair game.
No interview is the same. It depends on who interviews you and what background or topics s/he is most familiar with.

Thanks Andy - I'm thinking if they use platforms like HackerRank/CoderPad when it comes to progamming Qs

 

[Andy Nguyen]

Prior to the interview, you will get some info about your interviewer so check LinkedIn for his background.
Here is some info on RedNote from previous interview that gives you some idea. Translation is roughly from Chinese. I take no credit or claim on the accuracy of this whatsoever. This was supposed to be during April 2025.

The alumni interview consisted of 14-15 questions in 40 minutes:
1. Self-introduction
2. Two brain teasers, both relatively easy pigeonhole questions (brain teasers are almost always a must for traders)
3. Detailed analysis of two internship projects, focusing on why the xx method was used to do xx rather than what was done
4. Intuition of normal distribution and variance
5. Five assumptions of OLS, what the loss function is, and some simple follow-up questions
6. I've written many programming languages; when would I use Python, SQL, C++, and Rust?
7. Intuition of differentiation and integration
8. What papers have you recently read, who wrote them, how would you evaluate their contributions, and their guiding role in the quant field? (This was probably because I have a publication; they asked this)
9. QA summary (saving time): 1. Assessing breadth of knowledge, focusing on areas overlapping with the interviewer's field 2. The interviewer said they focused on intuition rather than calculation 3. Brain Teaser suggests focusing on reviewing the Pigeonhole Principle, the 12 types of Stirling numbers, and the recursive remainder theorem. Personally, I feel the Green Book can be used as the training set, 150 as the validation set, and Heard on the Street as the test set.

 

[eleayace]

Prior to the interview, you will get some info about your interviewer so check LinkedIn for his background.
Here is some info on RedNote from previous interview that gives you some idea. Translation is roughly from Chinese. I take no credit or claim on the accuracy of this whatsoever. This was supposed to be during April 2025.

The alumni interview consisted of 14-15 questions in 40 minutes:
1. Self-introduction
2. Two brain teasers, both relatively easy pigeonhole questions (brain teasers are almost always a must for traders)
3. Detailed analysis of two internship projects, focusing on why the xx method was used to do xx rather than what was done
4. Intuition of normal distribution and variance
5. Five assumptions of OLS, what the loss function is, and some simple follow-up questions
6. I've written many programming languages; when would I use Python, SQL, C++, and Rust?
7. Intuition of differentiation and integration
8. What papers have you recently read, who wrote them, how would you evaluate their contributions, and their guiding role in the quant field? (This was probably because I have a publication; they asked this)
9. QA summary (saving time): 1. Assessing breadth of knowledge, focusing on areas overlapping with the interviewer's field 2. The interviewer said they focused on intuition rather than calculation 3. Brain Teaser suggests focusing on reviewing the Pigeonhole Principle, the 12 types of Stirling numbers, and the recursive remainder theorem. Personally, I feel the Green Book can be used as the training set, 150 as the validation set, and Heard on the Street as the test set.

Very helpful info, thank you. I looked up on the interviewer and he has been an actuary, accountant, "AI engineer", CFA FRM and collecteur of multiple random online masters. The questions would likely be very different, but thanks for the example. Wish me luck.